| AVS 64th International Symposium & Exhibition | |
| Electronic Materials and Photonics Division | Wednesday Sessions |
| Session EM-WeM |
| Session: | Charge Transport in Disordered Materials |
| Presenter: | Junying Li, University of Florida |
| Authors: | J. Li, University of Florida C. Ying, University of Florida J. Hicks, University of Florida A. Ural, University of Florida |
| Correspondent: | Click to Email |
Transparent, conductive electrodes have many applications in electronic and photonic devices such as touch screens, solar cells, LEDs, and photodetectors. Metal nanowire networks are promising candidates for these applications as a replacement for indium tin oxide, which has problems such as high cost, scarcity, and brittleness. The conduction in metal nanowire networks is governed by percolation theory.
In most computational work, nanowires in these networks have been modeled as straight “sticks”. However, in real experiments, individual nanowires are not perfectly straight, but exhibit some degree of curviness. In this work, we perform systematic Monte Carlo simulations to study the effect of nanowire curviness on the scaling of percolation resistivity in nanowire networks. We generate the curved nanowires using 3rd-order Bézier curves. These curves are endowed with a curviness angle property that specifies how far away the two intermediate control points of the Bézier curve may lie, in the tangential sense, from a straight path connecting the two ends of the curve. The curviness angle is varied to obtain networks of differing values of curl ratio, which is defined as the ratio between the curved length of a nanowire and the straight distance between its two ends.
We find that, for random networks, the resistivity of the network increases with increasing nanowire curviness and the resistivity exhibits an inverse power law dependence on the curl ratio. We also find that the value of the extracted inverse power law critical exponent is not universal, but depends on other nanowire and device parameters. As a result, we also study the effect of nanowire density, nanowire length, device length, device width, and nanowire alignment angle on the scaling of network resistivity with nanowire curl ratio.
Curviness results in two competing effects on the percolation resistivity. First, curviness decreases the effective length of the nanowire between its two ends, which increases the resistivity. Second, it increases the effective width of the nanowire, which decreases the resistivity. For networks with aligned nanowires, we find that increasing the curviness decreases the resistivity, indicating that the second effect starts to dominate. By simulating networks with varying values of alignment angles, we study the crossover from the first to the second regime.
These results show how the degree of curviness of individual nanowires contributes to the macroscopic resistivity of the network. They also show that computational studies are an essential tool for providing insight into the percolation transport in transparent, conductive nanowire networks.